{"id":"https://openalex.org/W4309679261","doi":"https://doi.org/10.1109/smc53654.2022.9945292","title":"Deep Reinforcement-Learning-Based Adaptive Traffic Signal Control with Real-Time Queue Lengths","display_name":"Deep Reinforcement-Learning-Based Adaptive Traffic Signal Control with Real-Time Queue Lengths","publication_year":2022,"publication_date":"2022-10-09","ids":{"openalex":"https://openalex.org/W4309679261","doi":"https://doi.org/10.1109/smc53654.2022.9945292"},"language":"en","primary_location":{"id":"doi:10.1109/smc53654.2022.9945292","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53654.2022.9945292","pdf_url":null,"source":{"id":"https://openalex.org/S4363607746","display_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062088038","display_name":"Qi-Wei Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qi-Wei Sun","raw_affiliation_strings":["University of Jinan,Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan,China,250022"],"affiliations":[{"raw_affiliation_string":"University of Jinan,Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan,China,250022","institution_ids":["https://openalex.org/I34949971"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066065570","display_name":"Shiyuan Han","orcid":"https://orcid.org/0000-0001-6141-6497"},"institutions":[{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shi-Yuan Han","raw_affiliation_strings":["University of Jinan,Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan,China,250022"],"affiliations":[{"raw_affiliation_string":"University of Jinan,Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan,China,250022","institution_ids":["https://openalex.org/I34949971"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101912601","display_name":"Jin Zhou","orcid":"https://orcid.org/0000-0002-5353-2452"},"institutions":[{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Zhou","raw_affiliation_strings":["University of Jinan,Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan,China,250022"],"affiliations":[{"raw_affiliation_string":"University of Jinan,Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan,China,250022","institution_ids":["https://openalex.org/I34949971"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036479222","display_name":"Yuehui Chen","orcid":"https://orcid.org/0000-0002-5265-903X"},"institutions":[{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue-Hui Chen","raw_affiliation_strings":["University of Jinan,Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan,China,250022"],"affiliations":[{"raw_affiliation_string":"University of Jinan,Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan,China,250022","institution_ids":["https://openalex.org/I34949971"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101883518","display_name":"Kang Yao","orcid":"https://orcid.org/0009-0007-2483-4666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang Yao","raw_affiliation_strings":["Shandong Provincial Network Security and Infomationization Technology Center,Jinan,China,250002"],"affiliations":[{"raw_affiliation_string":"Shandong Provincial Network Security and Infomationization Technology Center,Jinan,China,250002","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5062088038"],"corresponding_institution_ids":["https://openalex.org/I34949971"],"apc_list":null,"apc_paid":null,"fwci":3.1838,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.94388271,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1760","last_page":"1765"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/queue","display_name":"Queue","score":0.8324881792068481},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8249513506889343},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7019108533859253},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5766050815582275},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5544593930244446},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.5527657866477966},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5238385796546936},{"id":"https://openalex.org/keywords/signal-timing","display_name":"Signal timing","score":0.46463727951049805},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45996126532554626},{"id":"https://openalex.org/keywords/adaptive-control","display_name":"Adaptive control","score":0.4431198239326477},{"id":"https://openalex.org/keywords/traffic-signal","display_name":"Traffic signal","score":0.4388420283794403},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41747209429740906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2735937237739563},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.2562272548675537},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.23126935958862305},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.15639469027519226},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15393292903900146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1032804548740387}],"concepts":[{"id":"https://openalex.org/C160403385","wikidata":"https://www.wikidata.org/wiki/Q220543","display_name":"Queue","level":2,"score":0.8324881792068481},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8249513506889343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7019108533859253},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5766050815582275},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5544593930244446},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5527657866477966},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5238385796546936},{"id":"https://openalex.org/C2776006172","wikidata":"https://www.wikidata.org/wiki/Q7512741","display_name":"Signal timing","level":3,"score":0.46463727951049805},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45996126532554626},{"id":"https://openalex.org/C107464732","wikidata":"https://www.wikidata.org/wiki/Q235781","display_name":"Adaptive control","level":3,"score":0.4431198239326477},{"id":"https://openalex.org/C2987419075","wikidata":"https://www.wikidata.org/wiki/Q8004","display_name":"Traffic signal","level":2,"score":0.4388420283794403},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41747209429740906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2735937237739563},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2562272548675537},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.23126935958862305},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.15639469027519226},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15393292903900146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1032804548740387},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc53654.2022.9945292","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53654.2022.9945292","pdf_url":null,"source":{"id":"https://openalex.org/S4363607746","display_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2020070197","https://openalex.org/W2074500080","https://openalex.org/W2095797625","https://openalex.org/W2145339207","https://openalex.org/W2153292056","https://openalex.org/W2154500405","https://openalex.org/W2498017881","https://openalex.org/W2548102009","https://openalex.org/W2604427121","https://openalex.org/W2761656023","https://openalex.org/W2766381093","https://openalex.org/W2907604339","https://openalex.org/W2915117209","https://openalex.org/W2919115771","https://openalex.org/W2964043796","https://openalex.org/W2997278929","https://openalex.org/W3014845548","https://openalex.org/W3047495191","https://openalex.org/W4214717370","https://openalex.org/W6674344953","https://openalex.org/W6692846177","https://openalex.org/W6736372492"],"related_works":["https://openalex.org/W2105464875","https://openalex.org/W2358880583","https://openalex.org/W2372489289","https://openalex.org/W2392754486","https://openalex.org/W3158967309","https://openalex.org/W2006393211","https://openalex.org/W2353721067","https://openalex.org/W1484104209","https://openalex.org/W2095996755","https://openalex.org/W2810423754"],"abstract_inverted_index":{"The":[0,23],"reinforcement":[1],"learning":[2],"(RL)":[3],"with":[4,79,155],"deep":[5,80],"neural":[6],"network,":[7],"as":[8,59,108],"a":[9],"data-driven":[10],"approach,":[11],"is":[12,62,89,114],"promising":[13],"for":[14,37,64,91,116],"adaptive":[15,53],"traffic":[16,21,54,67,117,141,163],"signal":[17,55,68,118],"control":[18,56,119],"(ATSC)":[19],"in":[20,69,97,158],"scenarios.":[22],"majority":[24],"of":[25,46,140,150],"the":[26,47,66,74,84,93,104,109,122,128,138,146,151],"existing":[27],"studies":[28],"focus":[29],"on":[30,121,137],"designing":[31],"efficient":[32],"agents":[33],"and":[34,148],"policy":[35],"optimization":[36],"ATSC,":[38],"but":[39],"neglect":[40],"to":[41],"observe":[42],"more":[43],"detailed":[44],"states":[45],"environment.":[48],"In":[49],"this":[50],"paper,":[51],"an":[52,70],"strategy,":[57],"named":[58],"A2C":[60,111],"RTQL,":[61],"proposed":[63,152],"scheduling":[65],"intersection,":[71],"by":[72,102],"combining":[73],"real-time":[75,94],"lane-based":[76],"queue":[77,95,106],"lengths":[78,96,107],"RL":[81],"agent.":[82],"First,":[83],"Lighthill-Whitham-Richards":[85],"(LWR)":[86],"shockwave":[87],"theory":[88],"employed":[90],"obtaining":[92],"each":[98],"lane.":[99],"After":[100],"that,":[101],"defining":[103],"obtained":[105],"inputs,":[110],"RTQL":[112],"strategy":[113,153],"designed":[115],"based":[120,136],"advanced":[123],"actor-critic":[124],"(A2C)":[125],"agent,":[126],"where":[127],"lanes":[129],"are":[130],"divided":[131],"into":[132],"multiple":[133],"parallel":[134],"environments":[135],"phases":[139],"signal.":[142],"Simulation":[143],"results":[144],"demonstrate":[145],"optimality":[147],"efficiency":[149],"compared":[154],"other":[156],"methods":[157],"SUMO":[159],"under":[160],"simulated":[161],"peak-hour":[162],"dynamics.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}